Overview

Brought to you by YData

Dataset statistics

Number of variables9
Number of observations18396
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory682.7 KiB
Average record size in memory38.0 B

Variable types

Text1
Numeric8

Alerts

num_100 is highly overall correlated with num_unq and 1 other fieldsHigh correlation
num_25 is highly overall correlated with num_50 and 1 other fieldsHigh correlation
num_50 is highly overall correlated with num_25High correlation
num_unq is highly overall correlated with num_100 and 2 other fieldsHigh correlation
total_secs is highly overall correlated with num_100 and 1 other fieldsHigh correlation
num_75 is highly skewed (γ1 = 24.2892902) Skewed
num_985 is highly skewed (γ1 = 34.34348763) Skewed
num_25 has 4970 (27.0%) zeros Zeros
num_50 has 9157 (49.8%) zeros Zeros
num_75 has 10345 (56.2%) zeros Zeros
num_985 has 10033 (54.5%) zeros Zeros
num_100 has 484 (2.6%) zeros Zeros

Reproduction

Analysis started2025-03-15 22:20:09.525421
Analysis finished2025-03-15 22:20:23.422516
Duration13.9 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

msno
Text

Distinct18209
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size287.4 KiB
2025-03-16T03:50:23.673981image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length44
Median length44
Mean length44
Min length44

Characters and Unicode

Total characters809424
Distinct characters65
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18025 ?
Unique (%)98.0%

Sample

1st rowWvbtqrNMRNsw8d16Q8bXCnQ5Doy6EpRZJsJukiPiPQA=
2nd row45G4M13aqDprqi2Qc94cqeKN3if+bYOtwX877yWSN78=
3rd rowtYCa8ga3FGwCXQRUqhd/mf2tkVrzRIkT/FY/B1MKZVE=
4th rowiV5B5c2Mnm9J3/MYJhjcvdCGMb54O+ZmYwxzLlkh4HY=
5th rowv/zXD/h3eH0A/oomcOo24hPP+xjKvrBuCtA01gbUXQQ=
ValueCountFrequency (%)
bavrqnz7x89pl5dsgqrbo52/4vvmqdim/jjcqjn0aqy 3
 
< 0.1%
ss/6ncfhmei82z/mzv5+ha99y2ebmvyf+w0gzzo4frg 3
 
< 0.1%
bakcjzxsmcjtolpenwularo4g0n0hg9kwbsmgx8qrn0 3
 
< 0.1%
l0bk3qozevb0csylngtu99ucgmfzowcjwgk+qbl97l8 2
 
< 0.1%
ggchieqc2u5gawlbrkbjdmqhf20jtezmz7wbapllumi 2
 
< 0.1%
maqcn/fzlwgdagpskk8kjwzmycyfzwfim1tgz28w+qy 2
 
< 0.1%
ay0nm//wvt3k+pvidmdidbtifmccmyf634gkntccpre 2
 
< 0.1%
hb3cpqzrstxhogq1ag5hhww9cxpoxwency8ldlt+czc 2
 
< 0.1%
6xp5ddodwu1hymmus2yc8vcjucrhfezuoxztj+oms78 2
 
< 0.1%
rd5alwmicnrev5jmg0xenkco41qtzcmtmzx9mdullms 2
 
< 0.1%
Other values (18199) 18373
99.9%
2025-03-16T03:50:24.156641image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
= 18396
 
2.3%
g 13411
 
1.7%
Q 13359
 
1.7%
U 13308
 
1.6%
M 13293
 
1.6%
8 13290
 
1.6%
I 13273
 
1.6%
E 13265
 
1.6%
k 13242
 
1.6%
o 13238
 
1.6%
Other values (55) 671349
82.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 321637
39.7%
Lowercase Letter 320781
39.6%
Decimal Number 124228
 
15.3%
Math Symbol 30680
 
3.8%
Other Punctuation 12098
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
g 13411
 
4.2%
k 13242
 
4.1%
o 13238
 
4.1%
s 13225
 
4.1%
c 13187
 
4.1%
w 13159
 
4.1%
l 12223
 
3.8%
a 12202
 
3.8%
h 12196
 
3.8%
p 12190
 
3.8%
Other values (16) 192508
60.0%
Uppercase Letter
ValueCountFrequency (%)
Q 13359
 
4.2%
U 13308
 
4.1%
M 13293
 
4.1%
I 13273
 
4.1%
E 13265
 
4.1%
Y 13077
 
4.1%
A 12810
 
4.0%
S 12279
 
3.8%
T 12185
 
3.8%
W 12148
 
3.8%
Other values (16) 192640
59.9%
Decimal Number
ValueCountFrequency (%)
8 13290
10.7%
0 13183
10.6%
4 13153
10.6%
5 12211
9.8%
9 12197
9.8%
1 12118
9.8%
3 12089
9.7%
6 12075
9.7%
7 11970
9.6%
2 11942
9.6%
Math Symbol
ValueCountFrequency (%)
= 18396
60.0%
+ 12284
40.0%
Other Punctuation
ValueCountFrequency (%)
/ 12098
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 642418
79.4%
Common 167006
 
20.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
g 13411
 
2.1%
Q 13359
 
2.1%
U 13308
 
2.1%
M 13293
 
2.1%
I 13273
 
2.1%
E 13265
 
2.1%
k 13242
 
2.1%
o 13238
 
2.1%
s 13225
 
2.1%
c 13187
 
2.1%
Other values (42) 509617
79.3%
Common
ValueCountFrequency (%)
= 18396
11.0%
8 13290
 
8.0%
0 13183
 
7.9%
4 13153
 
7.9%
+ 12284
 
7.4%
5 12211
 
7.3%
9 12197
 
7.3%
1 12118
 
7.3%
/ 12098
 
7.2%
3 12089
 
7.2%
Other values (3) 35987
21.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 809424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
= 18396
 
2.3%
g 13411
 
1.7%
Q 13359
 
1.7%
U 13308
 
1.6%
M 13293
 
1.6%
8 13290
 
1.6%
I 13273
 
1.6%
E 13265
 
1.6%
k 13242
 
1.6%
o 13238
 
1.6%
Other values (55) 671349
82.9%

date
Real number (ℝ)

Distinct31
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20170316
Minimum20170301
Maximum20170331
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size215.6 KiB
2025-03-16T03:50:24.363808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum20170301
5-th percentile20170302
Q120170308
median20170316
Q320170324
95-th percentile20170330
Maximum20170331
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.9393769
Coefficient of variation (CV)4.4319469 × 10-7
Kurtosis-1.2046072
Mean20170316
Median Absolute Deviation (MAD)8
Skewness-0.0060746825
Sum3.7105313 × 1011
Variance79.91246
MonotonicityNot monotonic
2025-03-16T03:50:24.586314image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
20170322 647
 
3.5%
20170330 640
 
3.5%
20170316 636
 
3.5%
20170311 629
 
3.4%
20170321 626
 
3.4%
20170306 620
 
3.4%
20170307 611
 
3.3%
20170314 609
 
3.3%
20170329 606
 
3.3%
20170327 602
 
3.3%
Other values (21) 12170
66.2%
ValueCountFrequency (%)
20170301 579
3.1%
20170302 593
3.2%
20170303 584
3.2%
20170304 583
3.2%
20170305 572
3.1%
20170306 620
3.4%
20170307 611
3.3%
20170308 580
3.2%
20170309 552
3.0%
20170310 596
3.2%
ValueCountFrequency (%)
20170331 560
3.0%
20170330 640
3.5%
20170329 606
3.3%
20170328 593
3.2%
20170327 602
3.3%
20170326 591
3.2%
20170325 587
3.2%
20170324 594
3.2%
20170323 584
3.2%
20170322 647
3.5%

num_25
Real number (ℝ)

High correlation  Zeros 

Distinct127
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1488911
Minimum0
Maximum353
Zeros4970
Zeros (%)27.0%
Negative0
Negative (%)0.0%
Memory size179.6 KiB
2025-03-16T03:50:24.817594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q37
95-th percentile25
Maximum353
Range353
Interquartile range (IQR)7

Descriptive statistics

Standard deviation12.278412
Coefficient of variation (CV)1.9968498
Kurtosis102.23048
Mean6.1488911
Median Absolute Deviation (MAD)2
Skewness7.1209005
Sum113115
Variance150.7594
MonotonicityNot monotonic
2025-03-16T03:50:25.062163image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4970
27.0%
1 2752
15.0%
2 1919
 
10.4%
3 1376
 
7.5%
4 1068
 
5.8%
5 836
 
4.5%
6 679
 
3.7%
7 545
 
3.0%
8 518
 
2.8%
9 423
 
2.3%
Other values (117) 3310
18.0%
ValueCountFrequency (%)
0 4970
27.0%
1 2752
15.0%
2 1919
 
10.4%
3 1376
 
7.5%
4 1068
 
5.8%
5 836
 
4.5%
6 679
 
3.7%
7 545
 
3.0%
8 518
 
2.8%
9 423
 
2.3%
ValueCountFrequency (%)
353 1
< 0.1%
292 1
< 0.1%
289 1
< 0.1%
198 1
< 0.1%
195 1
< 0.1%
188 1
< 0.1%
178 1
< 0.1%
175 1
< 0.1%
165 1
< 0.1%
160 1
< 0.1%

num_50
Real number (ℝ)

High correlation  Zeros 

Distinct58
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4797239
Minimum0
Maximum143
Zeros9157
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size179.6 KiB
2025-03-16T03:50:25.312927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum143
Range143
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.8289568
Coefficient of variation (CV)2.5876158
Kurtosis342.43977
Mean1.4797239
Median Absolute Deviation (MAD)1
Skewness13.747568
Sum27221
Variance14.66091
MonotonicityNot monotonic
2025-03-16T03:50:25.566949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9157
49.8%
1 4232
23.0%
2 2018
 
11.0%
3 988
 
5.4%
4 642
 
3.5%
5 360
 
2.0%
6 242
 
1.3%
7 189
 
1.0%
8 123
 
0.7%
9 77
 
0.4%
Other values (48) 368
 
2.0%
ValueCountFrequency (%)
0 9157
49.8%
1 4232
23.0%
2 2018
 
11.0%
3 988
 
5.4%
4 642
 
3.5%
5 360
 
2.0%
6 242
 
1.3%
7 189
 
1.0%
8 123
 
0.7%
9 77
 
0.4%
ValueCountFrequency (%)
143 1
< 0.1%
133 1
< 0.1%
120 1
< 0.1%
114 1
< 0.1%
85 1
< 0.1%
77 1
< 0.1%
73 1
< 0.1%
71 2
< 0.1%
69 1
< 0.1%
67 1
< 0.1%

num_75
Real number (ℝ)

Skewed  Zeros 

Distinct32
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93705153
Minimum0
Maximum149
Zeros10345
Zeros (%)56.2%
Negative0
Negative (%)0.0%
Memory size179.6 KiB
2025-03-16T03:50:25.796011image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum149
Range149
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.2494009
Coefficient of variation (CV)2.4005093
Kurtosis1255.1908
Mean0.93705153
Median Absolute Deviation (MAD)0
Skewness24.28929
Sum17238
Variance5.0598046
MonotonicityNot monotonic
2025-03-16T03:50:26.014296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 10345
56.2%
1 4259
23.2%
2 1871
 
10.2%
3 879
 
4.8%
4 399
 
2.2%
5 248
 
1.3%
6 124
 
0.7%
7 95
 
0.5%
8 45
 
0.2%
9 31
 
0.2%
Other values (22) 100
 
0.5%
ValueCountFrequency (%)
0 10345
56.2%
1 4259
23.2%
2 1871
 
10.2%
3 879
 
4.8%
4 399
 
2.2%
5 248
 
1.3%
6 124
 
0.7%
7 95
 
0.5%
8 45
 
0.2%
9 31
 
0.2%
ValueCountFrequency (%)
149 1
< 0.1%
82 1
< 0.1%
80 1
< 0.1%
61 1
< 0.1%
51 1
< 0.1%
39 2
< 0.1%
37 1
< 0.1%
31 1
< 0.1%
25 1
< 0.1%
24 2
< 0.1%

num_985
Real number (ℝ)

Skewed  Zeros 

Distinct42
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0839856
Minimum0
Maximum219
Zeros10033
Zeros (%)54.5%
Negative0
Negative (%)0.0%
Memory size179.6 KiB
2025-03-16T03:50:26.251602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile4
Maximum219
Range219
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.6303008
Coefficient of variation (CV)3.3490303
Kurtosis1749.8834
Mean1.0839856
Median Absolute Deviation (MAD)0
Skewness34.343488
Sum19941
Variance13.179084
MonotonicityNot monotonic
2025-03-16T03:50:26.479179image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 10033
54.5%
1 4293
23.3%
2 1893
 
10.3%
3 904
 
4.9%
4 463
 
2.5%
5 281
 
1.5%
6 164
 
0.9%
7 116
 
0.6%
8 63
 
0.3%
9 51
 
0.3%
Other values (32) 135
 
0.7%
ValueCountFrequency (%)
0 10033
54.5%
1 4293
23.3%
2 1893
 
10.3%
3 904
 
4.9%
4 463
 
2.5%
5 281
 
1.5%
6 164
 
0.9%
7 116
 
0.6%
8 63
 
0.3%
9 51
 
0.3%
ValueCountFrequency (%)
219 1
< 0.1%
209 1
< 0.1%
181 1
< 0.1%
127 1
< 0.1%
81 1
< 0.1%
75 2
< 0.1%
63 2
< 0.1%
60 1
< 0.1%
57 1
< 0.1%
47 1
< 0.1%

num_100
Real number (ℝ)

High correlation  Zeros 

Distinct271
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.005545
Minimum0
Maximum580
Zeros484
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size215.6 KiB
2025-03-16T03:50:26.713437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median16
Q337
95-th percentile111
Maximum580
Range580
Interquartile range (IQR)30

Descriptive statistics

Standard deviation38.756431
Coefficient of variation (CV)1.2916423
Kurtosis15.744164
Mean30.005545
Median Absolute Deviation (MAD)12
Skewness3.0614821
Sum551982
Variance1502.061
MonotonicityNot monotonic
2025-03-16T03:50:26.957493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 778
 
4.2%
4 707
 
3.8%
3 662
 
3.6%
2 658
 
3.6%
5 640
 
3.5%
6 601
 
3.3%
8 586
 
3.2%
7 576
 
3.1%
10 544
 
3.0%
9 508
 
2.8%
Other values (261) 12136
66.0%
ValueCountFrequency (%)
0 484
2.6%
1 778
4.2%
2 658
3.6%
3 662
3.6%
4 707
3.8%
5 640
3.5%
6 601
3.3%
7 576
3.1%
8 586
3.2%
9 508
2.8%
ValueCountFrequency (%)
580 1
< 0.1%
487 1
< 0.1%
471 1
< 0.1%
456 2
< 0.1%
451 1
< 0.1%
422 1
< 0.1%
384 1
< 0.1%
379 1
< 0.1%
365 1
< 0.1%
361 1
< 0.1%

num_unq
Real number (ℝ)

High correlation 

Distinct231
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.123559
Minimum1
Maximum399
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size179.6 KiB
2025-03-16T03:50:27.238523image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median18
Q338
95-th percentile97
Maximum399
Range398
Interquartile range (IQR)30

Descriptive statistics

Standard deviation32.194732
Coefficient of variation (CV)1.1054532
Kurtosis9.5023537
Mean29.123559
Median Absolute Deviation (MAD)12
Skewness2.4647719
Sum535757
Variance1036.5008
MonotonicityNot monotonic
2025-03-16T03:50:27.613086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 807
 
4.4%
2 650
 
3.5%
4 611
 
3.3%
3 586
 
3.2%
5 580
 
3.2%
8 561
 
3.0%
7 560
 
3.0%
10 541
 
2.9%
6 538
 
2.9%
9 486
 
2.6%
Other values (221) 12476
67.8%
ValueCountFrequency (%)
1 807
4.4%
2 650
3.5%
3 586
3.2%
4 611
3.3%
5 580
3.2%
6 538
2.9%
7 560
3.0%
8 561
3.0%
9 486
2.6%
10 541
2.9%
ValueCountFrequency (%)
399 1
< 0.1%
364 1
< 0.1%
334 1
< 0.1%
333 1
< 0.1%
312 1
< 0.1%
295 1
< 0.1%
293 1
< 0.1%
291 1
< 0.1%
287 1
< 0.1%
285 1
< 0.1%

total_secs
Real number (ℝ)

High correlation 

Distinct18264
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7847.7317
Minimum0.287
Maximum113437.88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size215.6 KiB
2025-03-16T03:50:27.937840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.287
5-th percentile416.02201
Q11932.0125
median4507.7861
Q39781.4348
95-th percentile28251.478
Maximum113437.88
Range113437.59
Interquartile range (IQR)7849.4223

Descriptive statistics

Standard deviation9473.6768
Coefficient of variation (CV)1.2071866
Kurtosis9.701086
Mean7847.7317
Median Absolute Deviation (MAD)3174.3303
Skewness2.6050677
Sum1.4436687 × 108
Variance89750544
MonotonicityNot monotonic
2025-03-16T03:50:28.172362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
247.6399994 6
 
< 0.1%
219 5
 
< 0.1%
261.223999 4
 
< 0.1%
245 4
 
< 0.1%
245.5740051 3
 
< 0.1%
2006 3
 
< 0.1%
280 3
 
< 0.1%
735 3
 
< 0.1%
256 3
 
< 0.1%
251 3
 
< 0.1%
Other values (18254) 18359
99.8%
ValueCountFrequency (%)
0.2870000005 1
< 0.1%
0.5289999843 1
< 0.1%
0.55400002 1
< 0.1%
0.7310000062 1
< 0.1%
1.08099997 1
< 0.1%
1.136999965 1
< 0.1%
1.207000017 1
< 0.1%
1.567000031 2
< 0.1%
1.593000054 1
< 0.1%
1.697999954 1
< 0.1%
ValueCountFrequency (%)
113437.875 1
< 0.1%
86680.0625 1
< 0.1%
86277.875 1
< 0.1%
85840 1
< 0.1%
85416.50781 1
< 0.1%
85105.22656 1
< 0.1%
82204.1875 1
< 0.1%
81671.70312 1
< 0.1%
81652.17188 1
< 0.1%
81483.5 1
< 0.1%

Interactions

2025-03-16T03:50:21.428847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:10.753024image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:12.216544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:13.674720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:15.201326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:16.639060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:18.236073image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:19.996752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:21.597566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:10.929864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:12.416788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:13.866036image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:15.394976image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:16.819303image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:18.473742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:20.171511image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:21.837406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:11.105193image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:12.618330image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:14.063365image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:15.596805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:17.007638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:18.703890image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:20.362816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:22.075723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:11.305560image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:12.794657image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:14.258259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:15.788456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:17.197379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:18.966229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:20.539864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:22.259530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:11.516296image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:12.967032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:14.429162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:15.957345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:17.414307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:19.216209image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:20.725162image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:22.408634image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:11.673428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:13.120139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:14.584114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:16.116151image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:17.581138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:19.443302image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:20.883021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:22.620959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:11.861552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:13.299148image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:14.752587image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:16.293757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:17.792419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:19.660706image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:21.064434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:22.802380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:12.046943image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:13.500834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:14.997847image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:16.477325image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:18.027734image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:19.834900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2025-03-16T03:50:21.254377image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2025-03-16T03:50:28.336343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
datenum_100num_25num_50num_75num_985num_unqtotal_secs
date1.0000.0100.0200.0030.0010.0060.0140.010
num_1000.0101.0000.2850.2500.2720.2820.8390.983
num_250.0200.2851.0000.5570.4880.4800.5240.350
num_500.0030.2500.5571.0000.4550.4160.4260.325
num_750.0010.2720.4880.4551.0000.4030.4040.345
num_9850.0060.2820.4800.4160.4031.0000.4010.362
num_unq0.0140.8390.5240.4260.4040.4011.0000.873
total_secs0.0100.9830.3500.3250.3450.3620.8731.000

Missing values

2025-03-16T03:50:23.011635image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-16T03:50:23.282246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

msnodatenum_25num_50num_75num_985num_100num_unqtotal_secs
8062169WvbtqrNMRNsw8d16Q8bXCnQ5Doy6EpRZJsJukiPiPQA=201703091011361242.812012
1334379945G4M13aqDprqi2Qc94cqeKN3if+bYOtwX877yWSN78=201703260011772164.858887
7589253tYCa8ga3FGwCXQRUqhd/mf2tkVrzRIkT/FY/B1MKZVE=20170321140029252691.107910
6095351iV5B5c2Mnm9J3/MYJhjcvdCGMb54O+ZmYwxzLlkh4HY=2017030251006121508.790039
13859296v/zXD/h3eH0A/oomcOo24hPP+xjKvrBuCtA01gbUXQQ=20170316110010122593.332031
3809066bY9ZrG+DXszUhOiGpieExVXO0Knyj2yMRxIb58qnXko=201703041123216414542875.652344
4358245LhrVcrU9Abgbje+5T17v8OrqYut8j6l3ruFobV7Jjko=201703290000441077.183960
15544009TwZeZfhR3GBdDgnj5sxTz/5i6syNokHayWIP60V/TdM=2017030641106101701.588013
508878VWi1+9mZGiok+ioVzUXVzkJFPa5Jk7HjlnnddT1cAYQ=2017031918210819121271.046875
8432658TZEBCR88vUTAvp3coFoTfCi5sDY5TloypuG63NXte80=201703039000481063.875000
msnodatenum_25num_50num_75num_985num_100num_unqtotal_secs
1634282jlW7InbX/3frDu931WPhw5j/SR4p/FxcJd9kEkyD/oE=201703251030117285818.110840
15942545kWQdziv0cpvvs7JQXxl2eQ6bThLq6wd1D3PZwed1sXk=20170311121031536215821.320312
16540782zZgzSwC4U6rqIai9a5xp7Q1A7RT2N57yIhiZbkB24y0=2017030584129203039.791016
18221828c+dla9srl9jkqcPPoddptkEmEffrf/we69chgIjKmVY=201703272000757517061.246094
5101948YWxPiur0s9nhlXeOotfYMZwWI2ZEQfNPFbnkRgJVgXE=20170302000033337056.446777
102901793xUv1NDdMxqY6blRHFCHiF6HpVBttbFqsAgmnXfuRc4=201703142322114374262.214844
4748764uzHktlZdSSv/0QmsqNRpWMNoDX45E2121mMOpipJLZQ=20170317000034309485.052734
12117141RF1iIhxrSRIjVj448+did/XwAkCtp8PUrpcRlj8y0m0=201703071000781869.670044
1515233085fjQZRYZrehWklAYMuyYAty81b6JdsCdFX7hmMyeD4=20170320100022203901.335938
6748790DX3FZEazcSCmmkZe/vcHL1kkyOlcHXRu+mSlcPz8dqk=201703242222191272.676025